This CNN-based model for recognition of hand written digits attains a validation accuracy of 99.2% after training for 12 epochs. Its trained on the MNIST dataset on Kaggle.

Hand Written Digit Recognition

基于OpenCV手写数字识别系统

A Hand Written Digit Recognition app trained on the MNIST dataset of Keras using the CNN model. skills used are Tensorflow, HTML,CSS,javascript.

Hand written digits recognition on STM32F4

基于BP神经网络实现手写数字识别与GUI可视化显示，实现工具：matlab

Hand written digit recognition implementation with different models

The project of recognizing handwritten digits found in mnist dataset by the use of Neural Networks(Feedforward Fully Connected)

Hand written digit recognition with SVM, Decision Tree and Random Forests

A Verilog implementation of a hand-written digit recognition Neural Network

A c++ implementation of Convolutional neural network, with a MNIST hand-written digits recognition application

No description available

Hand written digit recognition

a python program for hand written digits recognition

No description available

No description available

Hand-written digit recognition system for Python.

基于深度学习的手写数字识别

It is Described as: 1) —Digital documents are easier to store and process. The task of taking a decision to identify the character can be accomplished by using Recognition is to make editable documents from the existing paper documents or image files by employing automatic classification methods to so that various operations can be implemented on the document with ease. 2)-It reduces human efforts to a large extent, making work more reliable and time-efficient. A research was taken on to identify the gaps in the existing methodologies to come up with a solution. Recognito identifies isolated integral values by making use of Convolution Neural Network and Supervised Learning algorithms. 3)-It learns from the regular training of database by inputs provided to it and, thus increasing the reliability and accuracy gradually.

Linear regression, logistic regression, polynomial regression, multiclass classification, neural networks, KMeans, Principle Component Analysis (PCA), and Support Vector Machine (SVM). Fun machine learning applications: hand-written digit recognition model, spam email filter, image compression, anomaly detection model, and movie recommendation system.

This project performs digit recognition using deep learning concepts. It can classify an image into 10 classes.We have built a Multilayer perceptron(MLP) using most popular Google library Tensorflow to recognize handwritten digits.

MNIST hand-written digit recognition by fully-connected and convolutional neural networks - boiler plate code for easy reproduction and tutorial purpose.

By using the dataset of coursera and also using some other sources from internet to optimize the controlling parameters I have made the hand written digit recognition pattern.

This is a project about using GANs for hand written digit recognition.

This is a repository with various classifier written in jupyter notebook that works on hand written digits images.

Hand written character and digit recognition

Digit recognition using neural networks in kotlin

DBRHD 手写数字识别模型: sklearn实现的KNN，sklearn实现的MLP，NumPy实现的MLP，PyTorch实现的CNN

This is a simple handwritten digit recognition project in which you can provide any black and white picture as the input and it will output the probability of it being the numbers from 0-9, and also the most probable digit that it is predicting it to be.

MNIST hand written digit recognition using AlexNet and LeNet5 architecture